Thermal Load Forecasting Model of Heating System Based on Genetic Algorithm Optimization BP Neural Network

For short-term forecasting heating system heat load problem,use of improved genetic algorithm( GA) to initial weights and network structure to optimize BP( back propagation) neural network,and retain the best individual approach taken in the process of evolution. The method overcomes the randomness and the network structure of BP network training process is generally the initial weights of the network caused by the shock,and general BP network is easy to fall into local minima problems. Combined with the general method of BP neural network simulation and analysis and comparison results show that the method has global search capability,high forecast accuracy,absolute and relative error is small,fast convergence,can effectively heat load for the heating system for short-term forecasting.